Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	140,446
2	Rhode Island	138,970
3	South Dakota	138,163
4	Utah	123,541
5	Tennessee	121,478
6	Arizona	118,175
7	Iowa	115,220
8	Nebraska	113,259
9	Oklahoma	113,064
10	Wisconsin	113,025
11	South Carolina	111,864
12	New Jersey	111,572
13	Arkansas	111,007
14	Alabama	107,421
15	Indiana	106,890
16	Kansas	106,412
17	Delaware	106,394
18	Illinois	104,913
19	Idaho	104,697
20	New York	104,642
21	Mississippi	104,550
22	Florida	103,239
23	Nevada	101,970
24	Montana	101,426
25	Minnesota	101,176
26	Georgia	100,927
27	Wyoming	100,012
28	Kentucky	99,663
29	Texas	99,411
30	Massachusetts	99,329
31	Louisiana	98,280
32	Missouri	97,516
33	Connecticut	94,617
34	California	94,558
35	New Mexico	93,950
36	North Carolina	92,553
37	Michigan	92,408
38	Alaska	91,729
39	Ohio	91,304
40	Pennsylvania	89,429
41	Colorado	88,232
42	West Virginia	84,729
43	Virginia	76,886
44	Maryland	73,553
45	New Hampshire	69,281
46	District of Columbia	67,263
47	Washington	52,579
48	Puerto Rico	51,242
49	Maine	44,955
50	Oregon	43,160
51	Vermont	36,338
52	Hawaii	22,881

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Michigan	393
2	Colorado	312
3	Rhode Island	281
4	Puerto Rico	270
5	Alaska	267
6	Connecticut	240
7	Pennsylvania	233
8	Delaware	231
9	Minnesota	226
10	North Carolina	216
11	Florida	208
12	Maine	202
13	Illinois	178
14	New York	178
15	Oregon	166
16	New Hampshire	162
17	Washington	153
18	Idaho	149
19	North Dakota	149
20	Massachusetts	147
21	Tennessee	145
22	West Virginia	145
23	Nevada	134
24	New Mexico	123
25	District of Columbia	119
26	Maryland	118
27	Indiana	116
28	Virginia	114
29	South Carolina	112
30	Texas	111
31	Ohio	109
32	Wisconsin	107
33	Wyoming	107
34	Vermont	104
35	Kentucky	97
36	Louisiana	96
37	Arizona	95
38	Georgia	94
39	South Dakota	91
40	Mississippi	90
41	Utah	88
42	Montana	87
43	Nebraska	87
44	Iowa	84
45	Oklahoma	71
46	Missouri	69
47	Alabama	54
48	Hawaii	54
49	Kansas	53
50	California	49
51	Arkansas	39
52	New Jersey	-194

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,866
2	New York	2,651
3	Massachusetts	2,548
4	Rhode Island	2,514
5	Mississippi	2,415
6	Arizona	2,373
7	Connecticut	2,262
8	Louisiana	2,226
9	South Dakota	2,216
10	Alabama	2,213
11	Pennsylvania	2,040
12	North Dakota	1,990
13	Indiana	1,974
14	New Mexico	1,927
15	Illinois	1,907
16	Arkansas	1,897
17	Iowa	1,879
18	Michigan	1,857
19	South Carolina	1,832
20	Georgia	1,827
21	Nevada	1,767
22	Tennessee	1,766
23	Texas	1,731
24	Kansas	1,719
25	Oklahoma	1,697
26	Delaware	1,660
27	Ohio	1,641
28	Florida	1,627
29	District of Columbia	1,558
30	California	1,555
31	Missouri	1,501
32	West Virginia	1,485
33	Kentucky	1,476
34	Montana	1,465
35	Maryland	1,437
36	Wisconsin	1,287
37	Minnesota	1,271
38	Virginia	1,262
39	Nebraska	1,222
40	Wyoming	1,221
41	North Carolina	1,202
42	Idaho	1,143
43	Colorado	1,111
44	New Hampshire	951
45	Washington	723
46	Puerto Rico	713
47	Utah	681
48	Oregon	593
49	Maine	578
50	Alaska	451
51	Vermont	392
52	Hawaii	336

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Alaska	5
2	Michigan	5
3	Kentucky	3
4	New Jersey	3
5	Virginia	3
6	Florida	2
7	Maryland	2
8	Missouri	2
9	Nevada	2
10	New Hampshire	2
11	New Mexico	2
12	New York	2
13	Pennsylvania	2
14	Colorado	1
15	Connecticut	1
16	Georgia	1
17	Idaho	1
18	Illinois	1
19	Indiana	1
20	Louisiana	1
21	Maine	1
22	Massachusetts	1
23	Minnesota	1
24	Mississippi	1
25	Nebraska	1
26	North Carolina	1
27	Ohio	1
28	Puerto Rico	1
29	Rhode Island	1
30	South Carolina	1
31	South Dakota	1
32	Tennessee	1
33	Texas	1
34	Washington	1
35	Wisconsin	1
36	Wyoming	1
37	Alabama	0
38	Arizona	0
39	Arkansas	0
40	California	0
41	Delaware	0
42	District of Columbia	0
43	Hawaii	0
44	Iowa	0
45	Kansas	0
46	Montana	0
47	North Dakota	0
48	Oklahoma	0
49	Oregon	0
50	Utah	0
51	Vermont	0
52	West Virginia	-28

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Chattahoochee	Georgia	357,752	1	99
Crowley	Colorado	356,542	2	99
Bent	Colorado	271,293	3	99
Dewey	South Dakota	247,963	4	99
Lincoln	Arkansas	245,700	5	99
Davidson	Tennessee	141,069	184	94
Richland	South Carolina	110,052	996	68
York	South Carolina	108,232	1063	66
Orange	California	84,970	2164	31
Pierce	Washington	53,486	2880	8

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Foard	Texas	8,658	1	99
Galax city	Virginia	8,035	2	99
Jerauld	South Dakota	7,948	3	99
Emporia city	Virginia	7,856	4	99
Hancock	Georgia	7,686	5	99
Orange	California	1,555	1821	42
Davidson	Tennessee	1,338	2074	33
York	South Carolina	1,324	2095	33
Richland	South Carolina	1,301	2117	32
Pierce	Washington	709	2745	12

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.